Brain iron deposition and movement disorders in hereditary haemochromatosis without liver failure: A cross‐sectional study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Hereditary haemochromatosis (HH) is the most common inherited disorder of systemic iron excess in Northern Europeans. Emerging evidence indicates that brain iron overload occurs in HH. Despite this observation, there is a paucity of literature regarding central neurological manifestations, in particular movement disorders, in HH. The current study documents deep gray matter (DGM) nuclei iron deposition, movement disorders, and clinicoradiological correlations in HH without liver failure. METHODS: This is a cross-sectional study. Consecutive subjects with HFE-haemochromatosis without liver disease were recruited from an outpatient gastroenterology clinic. Age- and sex-matched healthy controls (HCs) were enrolled. Iron content in individual DGM nuclei was measured as mean susceptibility on magnetic resonance imaging using quantitative susceptibility mapping-based regions of interest analysis. Occurrence and phenotype of movement disorders were documented and correlated with patterns of DGM nuclei iron deposition in subjects with HH. RESULTS: Fifty-two subjects with HH and 47 HCs were recruited. High magnetic susceptibility was demonstrated in several DGM nuclei in all HH subjects compared to HCs. Thirty-five subjects with HH had movement disorders. Magnetic susceptibility in specific DGM nuclei correlated with individual movement disorder phenotypes. Serum ferritin, phlebotomy frequency, and duration were poor predictors of brain iron deposition. CONCLUSIONS: Abnormal brain iron deposition can be demonstrated on imaging in all subjects with HH without liver failure. A significant proportion of these subjects manifest movement disorders. Peripheral iron measurements appear not to correlate with brain iron deposition. Therefore, routine neurological examination and quantitative brain iron imaging are recommended in all subjects with HH.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".